Several factors affect database performance, and one of the most critical is how efficiently your application manages database connections. When multiple clients connect to PostgreSQL simultaneously, creating a new
connection for each request can be resource-intensive and slow. This is where connection pooling comes into play. Connection pooling allows connections to be reused instead of creating a new one every time, reducing overhead and improving performance. In this blog, we’ll explore PgBouncer, a lightweight PostgreSQL connection pooler, and how to set it up for your environment. Continue reading “Complete Guide to Fixing PostgreSQL Performance with PgBouncer Connection Pooling”
Category: Data Engineering
Building a Scalable And Cost-Efficient BigQuery Platform: Architecture, Practices & Lessons
As data platforms evolve from proof-of-concept pipelines to business-critical systems, scaling BigQuery requires more than writing efficient SQL. Without the right architectural choices, governance, and monitoring, organizations often face unpredictable costs, query slowdowns, and operational instability.
This blog outlines a set of platform-level engineering decisions and best practices adopted to run BigQuery at scale—focused on performance, cost optimization, security and observability. Each practice is backed by real-world implementation examples. Continue reading “Building a Scalable And Cost-Efficient BigQuery Platform: Architecture, Practices & Lessons”
Technical Case Study: Amazon Redshift and Athena as Data Warehousing Solutions
Introduction
Modern data architectures demand flexible, scalable, and cost-effective solutions that can handle diverse analytical workloads. Amazon Web Services offers multiple data warehousing approaches that serve different needs:
- Amazon Redshift: A petabyte-scale, fully managed data warehouse designed for complex analytical queries
- Amazon Athena: A serverless query service that allows direct querying of data in S3.
Continue reading “Technical Case Study: Amazon Redshift and Athena as Data Warehousing Solutions”
End-to-End Data Pipeline for Real-Time Stock Market Data!
Transform your data landscape with powerful, flexible, and flexible data pipelines. Learn the data engineering strategies needed to effectively manage, process, and derive insights from comprehensive datasets.. Creating robust, scalable, and fault-tolerant data pipelines is a complex task that requires multiple tools and techniques.
Unlock the skills of building real-time stock market data pipelines using Apache Kafka. Follow a detailed step-by-step guide from setting up Kafka on AWS EC2 and learn how to connect it to AWS Glue and Athena for intuitive data processing and insightful analytics.
Continue reading “End-to-End Data Pipeline for Real-Time Stock Market Data!”
Stream PostgreSQL Data to S3 via Kafka Using JDBC and S3 Sink Connectors : Part 1
Step 1: Set up PostgreSQL with Sample Data
Before you can source data from PostgreSQL into Kafka, you need a running instance of PostgreSQL with some data in it. This step involves:
- Setting up PostgreSQL: You spin up a PostgreSQL container (using Docker) to simulate a production database. PostgreSQL is a popular relational database, and in this case, it serves as the source of your data.
- Create a database and tables: You define a schema with a table (e.g., users) to hold some sample data. The table contains columns like id, name, and email. In a real-world scenario, your tables could be more complex, but this serves as a simple example.
- Populate the table with sample data: By inserting some rows into the users table, you simulate real data that will be ingested into Kafka.
Continue reading “Stream PostgreSQL Data to S3 via Kafka Using JDBC and S3 Sink Connectors : Part 1”